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Google Cloud Healthcare Australia

vivaLAB

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Cut bioinformatics run costs by 70% per report with Gemini platform and ensured regulatory confidence through a secure-by-design Virtual Private Cloud architecture

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Cut bioinformatics run costs by 70% per report with Gemini platform and ensured regulatory confidence through a secure-by-design Virtual Private Cloud architecture
Reduced data maintenance costs by 30% using Cloud Storage
vivaLAB is on a mission to move precision healthcare out of the laboratory and into daily life. By integrating metagenomics (gut microbiome), metabolomics (metabolic health), and lifestyle data, the platform empowers individuals to manage…
vivaLAB's architecture was secure-by-design from day one. In implementing strict IAM policies and siloing data, the system de-identifies sensitive biological information so that Google Cloud only interacts with unique user IDs, ensuring…

Reported impact

Business outcome

Accelerated genomics annotation from eight weeks to under four weeks with BigQuery

  • Compliance
  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Cut bioinformatics run costs by 70% per report with Gemini platform and ensured regulatory confidence through a secure-by-design Virtual Private Cloud architecture Reduced data maintenance costs by 30% using Cloud Storage Enabled real-time data visualization using Cloud Run and Firebase Key components include Gemini platform coordinates multi-agent workflows, tool calling, and governed automation across the application; BigQuery stores and queries the analytics and AI-ready datasets that power the workload; Cloud Storage stores large files, lake data, or static assets that feed downstream analytics and applications; Cloud Run runs serverless or virtualized application services with elastic scale for production AI workloads; Firebase provides the application backend and mobile services that connect customer-facing experiences to the platform. Reported outcomes include Accelerated genomics annotation from eight weeks to under four weeks with BigQuery.

Solution architecture

6 components · 4 layers
  1. Compute
    • Cloud Run Runs serverless or virtualized application services with elastic scale for production AI workloads.
    • Gemma Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
  2. Storage
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
    • Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
  3. Serving
    • Firebase Provides the application backend and mobile services that connect customer-facing experiences to the platform.
  4. Orchestration
    • Gemini platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

  • As the immense volume of data led to insurmountable computing and storage bottlenecks, the attempts to convert siloed research tools into a consumer product meant the team had to find a breakthrough. Annotating every…
  • BigQuery was a decisive factor: it allows us to ingest gigabytes of raw genomic data and immediately run complex queries in a serverless environment. This architecture removed the heavy lifting of data engineering,…
  • Enabled real-time data visualization using Cloud Run and Firebase
  • vivaLAB is on a mission to move precision healthcare out of the laboratory and into daily life. By integrating metagenomics (gut microbiome), metabolomics (metabolic health), and lifestyle data, the platform empowers…
  • vivaLAB leverages Google Cloud to scale a digital-first ecosystem, moving healthcare from reactive to proactive care.

Evidence from the source

Accelerated genomics annotation from eight weeks to under four weeks with BigQuery
As the immense volume of data led to insurmountable computing and storage bottlenecks, the attempts to convert siloed research tools into a consumer product meant the team had to find a breakthrough. Annotating every…
Cut bioinformatics run costs by 70% per report with Gemini platform and ensured regulatory confidence through a secure-by-design Virtual Private Cloud architecture
Enabled real-time data visualization using Cloud Run and Firebase
Reduced data maintenance costs by 30% using Cloud Storage
vivaLAB is on a mission to move precision healthcare out of the laboratory and into daily life. By integrating metagenomics (gut microbiome), metabolomics (metabolic health), and lifestyle data, the platform empowers…
Transparency Evidence & methodology 86/100 reference value · 77% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value86/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Google Cloud

    vivaLAB customer story

    First-party
    https://cloud.google.com/customers/vivalab
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived May 11, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Oct 9, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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